Centralized ML/AI artifact management for reproducibility and collaboration

Track, store, and manage your machine learning artifacts — from model binaries to logs and checkpoints — in a unified workspace, ensuring reproducibility and seamless collaboration.

Artifact versioning and lineage

Model checkpoint tracking

Pipeline and toolchain integration

Artifact versioning and lineage

Kiroframe automatically captures and versions all key artifacts from your ML/AI workflows — including models, logs, evaluation reports, and training checkpoints. Each artifact is tied to the specific dataset, hyperparameters, and runtime environment from which it was generated, allowing teams to trace the full lineage and ensure reproducibility across projects. Whether you’re debugging a model, comparing experiments, or preparing for audits, every detail is just a click away.

Model checkpoint tracking

With Kiroframe, managing model checkpoints becomes effortless. You can configure your pipeline to automatically store checkpoints during training, making it easy to resume interrupted runs or iterate from a known state. This action not only speeds up experimentation but also provides greater visibility into model evolution across training stages.

Pipeline and toolchain integration

Kiroframe integrates seamlessly with popular ML frameworks like TensorFlow, PyTorch, and MLflow. Artifacts are logged automatically or uploaded via API, ensuring minimal disruption to existing workflows. With support for CI/CD pipelines and modern toolchains, your models and experiments move fluidly from training to deployment.

Centralized ML/AI artifact management for reproducibility and collaboration

Artifact versioning and lineage

Model checkpoint tracking

Pipeline and toolchain integration

Supported platforms